Backward-Time Lagrangian Stochastic Dispersion Models and Their Application to Estimate Gaseous Emissions

Flesch, Thomas K.; Wilson, John D.; Yee, Eugene

doi:10.1175/1520-0450(1995)034<1320:btlsdm>2.0.co;2

Cited by 347 publications

(240 citation statements)

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“…[13] As long as we consider only linear processes, it is possible to reverse a dispersion problem simply by running a Lagrangian particle dispersion model backward in time (see Flesch et al [1995] for a proof). In analogy to forward simulations we may call the particle cloud produced by a backward simulation a ''retroplume.''…”

Section: Backward Simulationsmentioning

confidence: 99%

“…We take emission source strengths from the EDGAR emission inventory and assume that the emissions mix instantaneously into the footprint layer. Our footprint concept is, except for the numerical implementation, equivalent to that of Flesch et al [1995], where particles take up emissions upon touching the ground. Note that it would be possible to also consider sinks (Seibert and Frank, submitted manuscript, 2003), but we focus here on CO, which has a lifetime longer than the transport times considered.…”

Section: Backward Simulationsmentioning

confidence: 99%

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A backward modeling study of intercontinental pollution transport using aircraft measurements

Stohl¹,

Forster²,

Eckhardt³

et al. 2003

J. Geophys. Res.

329

350

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[1] In this paper we present simulations with a Lagrangian particle dispersion model to study the intercontinental transport of pollution from North America during an aircraft measurement campaign over Europe. The model was used for both the flight planning and a detailed source analysis after the campaign, which is described here with examples from two episodes. Forward calculations of emission tracers from North America, Europe, and Asia were made in order to understand the transport processes. Both episodes were preceded by stagnant conditions over North America, leading to the accumulation of pollutants in the North American boundary layer. Both anthropogenic sources and, to a lesser extent, forest fire emissions contributed to this pollution, which was then exported by warm conveyor belts to the middle and upper troposphere, where it was transported rapidly to Europe. Concentrations of many trace gases (CO, NO y , CO 2 , acetone, and several volatile organic compounds; O 3 in one case) and of ambient atmospheric ions measured aboard the research aircraft were clearly enhanced in the pollution plumes compared to the conditions outside the plumes. Backward simulations with the particle model were introduced as an indispensable tool for a more detailed analysis of the plume's source region. They make trajectory analyses (which, to date, were mainly used to interpret aircraft measurement data) obsolete. Using an emission inventory, we could decompose the tracer mixing ratios at the receptors (i.e., along the flight tracks) into contributions from every grid cell of the inventory. For both plumes we found that emission sources contributing to the tracer concentrations over Europe were distributed over large areas in North America. In one case, sources in California, Texas, and Florida contributed almost equally, and smaller contributions were also made by other sources located between the Yucatan Peninsula and Canada. In the other case, sources in eastern North America, including moderate contributions from forest fires, were most important. The plume's maximum was mainly caused by anthropogenic emissions from the New York area. To our knowledge, this is the first case reported where a pollution plume from a megacity was reliably detected over another continent.

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Section: Backward Simulationsmentioning

confidence: 99%

Section: Backward Simulationsmentioning

confidence: 99%

A backward modeling study of intercontinental pollution transport using aircraft measurements

Stohl¹,

Forster²,

Eckhardt³

et al. 2003

J. Geophys. Res.

329

350

View full text Add to dashboard Cite

show abstract

“…However enclosure measurements may not always be representative of emissions at the field scale (Genermont and Cellier, 1997;Sintermann et al, 2012). The inverse dispersion method concerns the inferring of the atmospheric emission rate (Q) of localised gas sources from the excess concentration ( C) they cause above background, by modelling the C/Q relationship for a given source-receptor configuration and meteorological state (Flesch et al, 2004(Flesch et al, , 2014.…”

Section: Introductionmentioning

confidence: 99%

“…This leads to modelling results that vary widely, with local recapture ranging from 2 to 60 % within 2 km from the source (Loubet et al, 2006. Accordingly, the modelling of NH 3 deposition can be a challenging undertaking, with models ranging from simple steady-state canopy resistance models to dynamic, bi-directional, multilayer and multi-process chemical species schemes (Flechard et al, 2013). Local-scale deposition models may ignore the wet deposition process, as dry deposition is most likely the dominant deposition mechanism near sources .…”

Section: Introductionmentioning

confidence: 99%

Ammonia emissions from a grazed field estimated by miniDOAS measurements and inverse dispersion modelling

et al. 2017

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Abstract. Ammonia (NH 3 ) fluxes were estimated from a field being grazed by dairy cattle during spring by applying a backward Lagrangian stochastic model (bLS) model combined with horizontal concentration gradients measured across the field. Continuous concentration measurements at field boundaries were made by open-path miniDOAS (differential optical absorption spectroscopy) instruments while the cattle were present and for 6 subsequent days. The deposition of emitted NH 3 to "clean" patches on the field was also simulated, allowing both "net" and "gross" emission estimates, where the dry deposition velocity (v d ) was predicted by a canopy resistance (R c ) model developed from local NH 3 flux and meteorological measurements. Estimated emissions peaked during grazing and decreased after the cattle had left the field, while control on emissions was observed from covariance with temperature, wind speed and humidity and wetness measurements made on the field, revealing a diurnal emission profile. Large concentration differences were observed between downwind receptors, due to spatially heterogeneous emission patterns. This was likely caused by uneven cattle distribution and a low grazing density, where "hotspots" of emissions would arise as the cattle grouped in certain areas, such as around the water trough. The spatial complexity was accounted for by separating the model source area into sub-sections and optimising individual source area coefficients to measured concentrations. The background concentration was the greatest source of uncertainty, and based on a sensitivity/uncertainty analysis the overall uncertainty associated with derived emission factors from this study is at least 30-40 %.Emission factors can be expressed as 6 ± 2 g NH 3 cow −1 day −1 , or 9 ± 3 % of excreted urine-N emitted as NH 3 , when deposition is not simulated and 7 ± 2 g NH 3 cow −1 day −1 , or 10 ± 3 % of excreted urine-N emitted as NH 3 , when deposition is included in the gross emission model. The results suggest that around 14 ± 4 % of emitted NH 3 was deposited to patches within the field that were not affected by urine or dung.

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“…Since backward-in-time modelling is extensively used in many applications (e.g. Flesch et al 1995;Kljun et al 2002;Seibert and Frank 2004), for completeness and clarity we discuss in some detail the backward-in-time formulation of this model based on Thomson (1987), and we perform consistency tests between backward and forward simulations. The formulation of Luhar et al (1996) assumes that the turbulent statistics are horizontally homogeneous and stationary and, to our knowledge, many operational implementations of the Lagrangian stochastic model used nowadays neglect the horizontal and time derivatives of the turbulence statistics in the model formulation.…”

Section: Introductionmentioning

confidence: 99%

Lagrangian Stochastic Modelling of Dispersion in the Convective Boundary Layer with Skewed Turbulence Conditions and a Vertical Density Gradient: Formulation and Implementation in the FLEXPART Model

Cassiani

Stohl

Brioude

2014

Boundary-Layer Meteorol

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A correction for the vertical gradient of air density has been incorporated into a skewed probability density function formulation for turbulence in the convective boundary layer. The related drift term for Lagrangian stochastic dispersion modelling has been derived based on the well-mixed condition. Furthermore, the formulation has been extended to include unsteady turbulence statistics and the related additional component of the drift term obtained. These formulations are an extension of the drift formulation reported by Luhar et al. (Atmos Environ 30:1407-1418, 1996 following the well-mixed condition proposed by Thomson (J Fluid Mech 180:529-556, 1987). Comprehensive tests were carried out to validate the formulations including consistency between forward and backward simulations and preservation of a well-mixed state with unsteady conditions. The stationary state CBL drift term with density correction was incorporated into the FLEXPART and FLEXPART-WRF Lagrangian models, and included the use of an ad hoc transition function that modulates the third moment of the vertical velocity based on stability parameters. Due to the current implementation of the FLEXPART models, only a steady-state horizontally homogeneous drift term could be included. To avoid numerical instability, in the presence of non-stationary and horizontally inhomogeneous conditions, a re-initialization procedure for particle velocity was used. The criteria for re-initialization and resulting errors were assessed for the case of non-stationary conditions by comparing a reference numerical solution in simplified unsteady conditions, obtained using the non-stationary drift term, and a solution based on the steady drift with re-initialization. Two examples of "real-world" numerical simulations were performed under different convective conditions to demonstrate the effect of the vertical gradient in density on the particle dispersion in the CBL.

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Backward-Time Lagrangian Stochastic Dispersion Models and Their Application to Estimate Gaseous Emissions

Cited by 347 publications

References 0 publications

A backward modeling study of intercontinental pollution transport using aircraft measurements

A backward modeling study of intercontinental pollution transport using aircraft measurements

Ammonia emissions from a grazed field estimated by miniDOAS measurements and inverse dispersion modelling

Lagrangian Stochastic Modelling of Dispersion in the Convective Boundary Layer with Skewed Turbulence Conditions and a Vertical Density Gradient: Formulation and Implementation in the FLEXPART Model

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